Towards performing everyday manipulation activities

This article investigates fundamental issues in scaling autonomous personal robots towards open-ended sets of everyday manipulation tasks which involve high complexity and vague job specifications. To achieve this, we propose a control architecture that synergetically integrates some of the most promising artificial intelligence (AI) methods that we consider as necessary for the performance of everyday manipulation tasks in human living environments: deep representations, probabilistic first-order learning and reasoning, and transformational planning of reactive behavior — all of which are integrated in a coherent high-level robot control system: COGITO. We demonstrate the strengths of this combination of methods by realizing, as a proof of concept, an autonomous personal robot capable of setting a table efficiently using instructions from the world wide web. To do so, the robot translates instructions into executable robot plans, debugs its plan to eliminate behavior flaws cau...

• Type: Journal
• Year: 2010
• Where: RAS
• Authors: Michael Beetz, Dominik Jain, Lorenz Mösenlechner, Moritz Tenorth
• Keywords: Autonomous Personal Robot, Everyday Manipulation Tasks, RAS 2010, Robot